Thursday, 13 September 2012

Artificial Intelligence is all about bringing Common Sense, Expert Knowledge, and Superhuman Reasoning to Computers. For the most part, AI does not produce stand-alone systems, but instead adds knowledge and reasoning to existing applications, databases, and environments, to make them friendlier, smarter, and more sensitive to user behavior and changes in their environments.
In the domain of Artificial intelligence, various problem-solving techniques have been developed. Though working towards the common goal of making a computer 'intelligent', all these techniques use different methodologies. Case Based Reasoning is one of these techniques. Computer systems that solve new problems by analogy with old ones are often called Case Based Reasoning (CBR) systems.
This paper answers fundamental questions like what is CBR and how is it related to human reasoning, the different issues involved in developing a CBR system and CBR's comparison with other problem-solving techniques.
Case Based Reasoning (CBR) is a powerful technique to search and retrieve information from a collection of past experiences (cases). These technologies enable preserving and sharing best practices in service and diagnostic.
Consider a simple example of Case Based Reasoning (CBR) that deals with car diagnostics. A case stored in the case base is a fault that has been solved in the past. The case description is made up of effects, such as observed symptoms (e.g., engine does not start) and context parameters (e.g., ignition key is turned on). It can also include measured parameters for example, the state of the electronic control units obtained using testing equipment. The solution is the maintenance operation.
With CBR, you can make use of the experience captured in this case base to solve new diagnostic problems. If you encounter a new, unsolved diagnostic problem, a past case that is similar to your new problem will very likely contain an appropriate maintenance operation.
Analogy to human reasoning
When confronted with a new problem, a technician with no or little experience may attempt to analyze the problem using a Fault Isolation Manual, if there is one and if this is not an overly time-consuming task. He might also try to find the source of the problem by himself, in which case he may end up changing the wrong parts. Finally, he might ask for help, either by calling the car manufacturers technical support center or by asking a more experienced colleague.
A more-experienced mechanic can recall past cases he has solved. His intuitive thinking process is, "Have I ever seen a similar problem before? If so, what did I do to solve it?" If the more-experienced mechanic can find the solution and fix the car, his less-experienced colleague will learn from this new experience and build up his own memory of
solved cases. This human ability to learn is a key to human intelligence and
If the experience of its employees is indeed a valuable asset to a company, it makes sense to try to capture this experience and store it in such a way that it can be reused in the future and shared among the company's individuals. 




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